手法を比較

	Weakly Supervised GRU	Long Short-Term Memory (LSTM)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014–2016	1997
提唱者	Chung et al. (GRU); Ratner et al. (weak supervision framework)	Hochreiter, S. & Schmidhuber, J.
種類	Weakly supervised sequence model	Recurrent neural network with gated memory cells
原典	Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
別名	WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
関連	6	4
概要	Weakly Supervised GRU trains a Gated Recurrent Unit network on sequences labeled by imperfect, heuristic, or programmatic sources rather than costly hand-annotated ground truth. It combines the GRU's efficiency at capturing temporal dependencies with weak-supervision techniques that aggregate noisy labels, enabling practical sequence modeling when large fully labeled datasets are unavailable.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.